Meni (Menachem) Brief · Applied Scientist, AI/LLM · Seattle, WA
Applied scientist and end-to-end builder. 3+ years at Microsoft training and evaluating LLMs, most recently RL for model behavior in complex tool use: agent orchestration and MCP invocation. Then co-founded a startup and shipped a production mobile app as founding engineer and technical lead. I move fast from decision to execution.
email available on request · linkedin.com/in/meni-brief · scholar.google.com/citations?user=t-P-tjgAAAAJ · semanticscholar.org/author/Meni-Brief/2217252086
experience
ship(mobile_app → app_stores)
Founding engineer of a production-grade, mobile-first productivity app: from zero to live app-store releases with real users, leading a small team I hired. I own the system architecture end to end: Django backend on DigitalOcean, in-app AI processing, two-way sync with Google services, CI/CD on GitHub Actions, product analytics, and error monitoring, all on an AI-native development workflow that keeps a micro team shipping at production velocity.
simulate(stripe, slack).replay()
Proof-of-concept eval platform for AI agents: high-fidelity clones of real software platforms (Stripe, Slack) as controlled simulation environments, plus a deterministic capture-and-replay harness for reproducible agent runs.
rl.improve(tool_use, mcp)
Applied RL to improve LLM behavior in complex tool-use scenarios: agent orchestration and correct invocation of MCP tools. Led applied research on domain adaptation and knowledge injection: data curation, experiment design, training, evaluation, and results communication to internal and external stakeholders. Delivered LLM success stories in healthcare and financial services; co-authored research papers and one patent.
research(llm.applications)
Original research on LLM applications and capabilities across industries (PyTorch / Transformers / OpenAI), from experiment design through implementation; improved ML models running in production.
segment(ultrasound) → fda_cleared
Built deep-learning segmentation models for ultrasound imaging; the resulting tool (CNerve) passed FDA requirements for AI in healthcare. Served as data owner and collaborated directly with physicians.
research
A systematic comparison of unsupervised fine-tuning and retrieval-augmented generation for injecting knowledge into LLMs, across knowledge-intensive tasks, including entirely new facts models never saw in pre-training.
skills
education
MSc in Data Science (GPA 91.4). BSc in Information Systems Engineering, cum laude, #3 in class (GPA 90.6), with minors in Economics and Fintech.